• Title, Summary, Keyword: Model identification

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Identification of ARMAX Model and Linear Estimation Algorithm for Structural Dynamic Characteristics Analysis (구조동특성해석을 위한 ARMAX 모형의 식별과 선형추정 알고리즘)

  • Choe, Eui-Jung;Lee, Sang-Jo
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.7
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    • pp.178-187
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    • 1999
  • In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.

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A Study on the System Identification based on Neural Network for Modeling of 5.1. Engines (S.I. 엔진 모델링을 위한 신경회로망 기반의 시스템 식별에 관한 연구)

  • 윤마루;박승범;선우명호;이승종
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.5
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    • pp.29-34
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    • 2002
  • This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.

Hysteresis characterization and identification of the normalized Bouc-Wen model

  • Li, Zongjing;Shu, Ganping
    • Structural Engineering and Mechanics
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    • v.70 no.2
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    • pp.209-219
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    • 2019
  • By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.

System identification of high-rise buildings using shear-bending model and ARX model: Experimental investigation

  • Fujita, Kohei;Ikeda, Ayumi;Shirono, Minami;Takewaki, Izuru
    • Earthquakes and Structures
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    • v.8 no.4
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    • pp.843-857
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    • 2015
  • System identification is regarded as the most basic technique for structural health monitoring to evaluate structural integrity. Although many system identification techniques extracting mode information (e.g., mode frequency and mode shape) have been proposed so far, it is also desired to identify physical parameters (e.g., stiffness and damping). As for high-rise buildings subjected to long-period ground motions, system identification for evaluating only the shear stiffness based on a shear model does not seem to be an appropriate solution to the system identification problem due to the influence of overall bending response. In this paper, a system identification algorithm using a shear-bending model developed in the previous paper is revised to identify both shear and bending stiffnesses. In this algorithm, an ARX (Auto-Regressive eXogenous) model corresponding to the transfer function for interstory accelerations is applied for identifying physical parameters. For the experimental verification of the proposed system identification framework, vibration tests for a 3-story steel mini-structure are conducted. The test structure is specifically designed to measure horizontal accelerations including both shear and bending responses. In order to obtain reliable results, system identification theories for two different inputs are investigated; (a) base input motion by a modal shaker, (b) unknown forced input on the top floor.

Two-Phase Neuro-System Identification Based on Artificial System (모조 시스템 형성에 기반한 2단계 뉴로 시스템 인식)

  • 배재호;왕지남
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.3
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    • pp.107-118
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    • 1998
  • Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

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Structural Damage Identification by Using the Structurally Damped Spectral Element Model (구조감쇠가 고려된 스펙트럴요소 모델을 이용한 구조손상규명)

  • Kim, Jung-Soo;Cho, Joo-Yong;Lee, U-Sik
    • Proceedings of the KSR Conference
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    • pp.121-126
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    • 2004
  • In this paper, a nonlinear structural damage identification algorithm is derived by taking into account the structurally damped spectral element model thinking over a real situation. The structural damage identification analyses are conducted by using the Newton-Raphson method. It is found that, in general Structural Damage Identification by using the Structurally Damped Spectral Element Model provides the same exact damage identification results when compared with the results obtained by the structurally undamped spectral model.

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Design of Learning Fuzzy Controller by the Self-Tuning Algorithm for Equipment Systems (설비시스템을 위한 자기동조기법에 의한 학습 FUZZY 제어기 설계)

  • Lee, Seung
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.9 no.6
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    • pp.71-77
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    • 1995
  • This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

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Wavelet based system identification for a nonlinear experimental model

  • Li, Luyu;Qin, Han;Niu, Yun
    • Smart Structures and Systems
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    • v.20 no.4
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    • pp.415-426
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    • 2017
  • Traditional experimental verification for nonlinear system identification often faces the problem of experiment model repeatability. In our research, a steel frame experimental model is developed to imitate the behavior of a single story steel frame under horizontal excitation. Two adjustable rotational dampers are used to simulate the plastic hinge effect of the damaged beam-column joint. This model is suggested as a benchmark model for nonlinear dynamics study. Since the nonlinear form provided by the damper is unknown, a Morlet wavelet based method is introduced to identify the mathematical model of this structure under different damping cases. After the model identification, earthquake excitation tests are carried out to verify the generality of the identified model. The results show the extensive applicability and effectiveness of the identification method.

Crack identification based on Kriging surrogate model

  • Gao, Hai-Yang;Guo, Xing-Lin;Hu, Xiao-Fei
    • Structural Engineering and Mechanics
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    • v.41 no.1
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    • pp.25-41
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    • 2012
  • Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

The study on the efficient Identification Model of Nonlinear dynamical system using Neural Networks (신경회로망을 이용한 비선형 동적인 시스템의 효과적인 인식모델에 관한 연구)

  • 강동우;이상배
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.233-242
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    • 1995
  • In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

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